OmpSs: A PROPOSAL FOR PROGRAMMING HETEROGENEOUS MULTI-CORE ARCHITECTURES

Durán, Alejandro; Ayguadé, Eduard; Badía, Rosa M.; Labarta, Jesús; Martinell, Luis; Martorell, Xavier; Planas, Judit

doi:10.1142/s0129626411000151

Cited by 544 publications

(506 citation statements)

References 4 publications

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“…We also used the OmpSs programming model [7] and its associated toolflow: Nanos++ runtime system (version 0.7.5), Mercurium source-to-source compiler (version 1.99.6), and gcc 4.9.1 as the back-end compiler. To analyze the behavior of the benchmarks, we used the Extrae instrumentation package (version 2.5) and the Paraver trace viewer (version 4.5) [12].…”

Section: Discussionmentioning

confidence: 99%

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Evaluating the Impact of OpenMP 4.0 Extensions on Relevant Parallel Workloads

Vidal

Casas

Moretó

et al. 2015

OpenMP: Heterogenous Execution and Data Movements

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Abstract. OpenMP has been for many years the most widely used programming model for shared memory architectures. Periodically, new features are proposed and some of them are finally selected for inclusion in the OpenMP standard. The OmpSs programming model developed at the Barcelona Supercomputing Center (BSC) aims to be an OpenMP forerunner that handles the main OpenMP constructs plus some extra features not included in the OpenMP standard. In this paper we show the usefulness of three OmpSs features not currently handled by OpenMP 4.0 by deploying them over three applications of the PARSEC benchmark suite and showing the performance benefits. This paper also shows performance trade-offs between the OmpSs/OpenMP tasking and loop parallelism constructs and shows how a hybrid implementation that combines both approaches is sometimes the best option.

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Section: Discussionmentioning

confidence: 99%

“…The OmpSs programming model [6] developed at the Barcelona Supercomputing Center aims to be an OpenMP forerunner that handles the main OpenMP constructs plus other features not included in the OpenMP standard. OmpSs is based on #pragma annotations and its semantics are almost identical to the OpenMP standard.…”

Section: Introductionmentioning

confidence: 99%

Evaluating the Impact of OpenMP 4.0 Extensions on Relevant Parallel Workloads

Vidal

Casas

Moretó

et al. 2015

OpenMP: Heterogenous Execution and Data Movements

Self Cite

View full text Add to dashboard Cite

show abstract

“…Finally, in [23] the authors propose criticality-aware dynamic task scheduling on heterogeneous architectures with OmpSs [24,25]. The paper proposes scheduling policies in order to reduce the total execution time of a task represented by DAG.…”

Section: Related Workmentioning

confidence: 99%

Dynamic energy-aware scheduling for parallel task-based application in cloud computing

Juarez

Ejarque

Badía

2018

Future Generation Computer Systems

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181

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h i g h l i g h t s• Energy-aware run-time scheduler for task-based applications.• Model for estimating the application Energy consumption.• Methodology to automatically generate the required power consumption profile.• Multi-heuristic resource allocation algorithm to get solutions in polynomial time.• Energy saving/performance trade-off evaluation for different scenarios. a r t i c l e i n f o b s t r a c tGreen Computing is a recent trend in computer science, which tries to reduce the energy consumption and carbon footprint produced by computers on distributed platforms such as clusters, grids, and clouds. Traditional scheduling solutions attempt to minimize processing times without taking into account the energetic cost. One of the methods for reducing energy consumption is providing scheduling policies in order to allocate tasks on specific resources that impact over the processing times and energy consumption. In this paper, we propose a real-time dynamic scheduling system to execute efficiently taskbased applications on distributed computing platforms in order to minimize the energy consumption. Scheduling tasks on multiprocessors is a well known NP-hard problem and optimal solution of these problems is not feasible, we present a polynomial-time algorithm that combines a set of heuristic rules and a resource allocation technique in order to get good solutions on an affordable time scale. The proposed algorithm minimizes a multi-objective function which combines the energy-consumption and execution time according to the energy-performance importance factor provided by the resource provider or user, also taking into account sequence-dependent setup times between tasks, setup times and down times for virtual machines (VM) and energy profiles for different architectures. A prototype implementation of the scheduler has been tested with different kinds of DAG generated at random as well as on real task-based COMPSs applications. We have tested the system with different size instances and importance factors, and we have evaluated which combination provides a better solution and energy savings. Moreover, we have also evaluated the introduced overhead by measuring the time for getting the scheduling solutions for a different number of tasks, kinds of DAG, and resources, concluding that our method is suitable for run-time scheduling.

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“…OmpSs [7] is based on the OpenMP programming model with modifications to its execution model. It is primarily a task-based programming model focusing on abstraction of details to the user thereby making the programmer to write code in sequential flow with annotated pragmas for tasks(parallel regions).…”

Section: Ompss-opencl Modelmentioning

confidence: 99%

Scalability and Parallel Execution of OmpSs-OpenCL Tasks on Heterogeneous CPU-GPU Environment

Elangovan

Badía

Ayguadé

2014

Lecture Notes in Computer Science

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Abstract. With heterogeneous computing becoming mainstream, researchers and software vendors have been trying to exploit the best of the underlying architectures like GPUs or CPUs to enhance performance. Parallel programming models play a crucial role in achieving this enhancement. One such model is OpenCL, a parallel computing API for cross platform computations targeting heterogeneous architectures. However, OpenCL is a low-level programming language, therefore it can be time consuming to directly develop OpenCL code. To address this shortcoming, OpenCL has been integrated with OmpSs, a task-based programming model to provide abstraction to the user thereby reducing programmer effort. OmpSs-OpenCL programming model deals with a single OpenCL device either a CPU or a GPU. In this paper, we upgrade OmpSs-OpenCL programming model by supporting parallel execution of tasks across multiple CPU-GPU heterogeneous platforms. We discuss the design of the programming model along with its asynchronous runtime system. We investigated scalability of four OmpSs-OpenCL benchmarks across 4 GPUs gaining speedup of up to 4x. Further, in order to achieve effective utilization of the computing resources, we present static and work-stealing scheduling techniques. We show results of parallel execution of applications using OmpSs-OpenCL model and use heterogeneous workloads to evaluate our scheduling techniques on a heterogeneous CPU-GPU platform.

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OmpSs: A PROPOSAL FOR PROGRAMMING HETEROGENEOUS MULTI-CORE ARCHITECTURES

Cited by 544 publications

References 4 publications

Evaluating the Impact of OpenMP 4.0 Extensions on Relevant Parallel Workloads

Evaluating the Impact of OpenMP 4.0 Extensions on Relevant Parallel Workloads

Dynamic energy-aware scheduling for parallel task-based application in cloud computing

Scalability and Parallel Execution of OmpSs-OpenCL Tasks on Heterogeneous CPU-GPU Environment

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